(cache)Toxicity Moderation

Abstract

The rapid rise of social media has brought with it numerous challenges in moderating toxic and harmful content. Given the interconnected nature of social media sites and the ease with which users can move between platforms, overly strict moderation may push users to migrate to more toxic environments.

This demonstrates an Agent-Based Approach for modeling this migration and the impacts that different moderation techniques may have on social media users. Using the Construct simulation framework, I've modeled user behavior across multiple platforms to attempt to better understand how varying moderation policies affect both individual platforms and the broader social media ecosystem.

Research Focus

Primary Questions

• How do moderation strategies impact system-wide toxicity?
• What are the patterns of user migration between platforms?

Methodology

• Agent-based simulation using Construct framework
• Measured impacts across moderation strategy, strictness, and accuracy

Platform Impact

Moderation strategies showed varying impacts on platform toxicity:

• The distribution of the users across toxicity levels was the most important determinant of platform toxicity.
• The moderation strategiy also had a major impact on overall toxicity levels.
• Individual platform toxicity was easier to control, while overall toxicity exhibited more complex relationships

User Behavior

User responses to moderation varied by strictness and moderation techniques:

• Strict moderation led to increased user migration
• Soft moderation techniques like warnings and temporary bans mitigated these migration patterns

Key Implications

While strict moderation can effectively reduce toxicity on individual platforms, the migration of users to less moderated platforms could create challenges for the broader social media ecosystem. This tentatively suggests the need for coordinated cross-platform moderation strategies to effectively address online toxicity.

1. Introduction

1.1 Larger Context and Motivation

Social media is central to modern information dissemination, public opinion shaping, and social connection. However, this landscape faces significant challenges from malicious actors spreading toxicity and misinformation.

Toxic content like hate speech has been linked to real-world violence [21].
Disinformation campaigns actively try to manipulate public opinion on critical issues [8].
The rise of LLMs and chatbots accelerates the volume and speed of content generation, increasing the need for effective countermeasures [10].
Moderation involves complex technical, political, and behavioral challenges.
Techniques like de-platforming can reduce harm on a specific site but risk pushing users to less moderated, potentially more toxic environments [2].

1.2 Specific Problem Description & Approach

This paper aims to model the impact of content moderation strategies on user behavior, individual platform toxicity, and overall toxicity across a network of social media sites.

The study utilizes an Agent-Based Model (ABM) built using the Construct framework [7]. This involves simulating user banning, migration between platforms with varying moderation levels, defining a model for message and user toxicity, and comparing toxicity metrics at agent, network, and system levels.

Key Research Questions:

How do different moderation strategies (e.g., banning, warnings) on large platforms affect overall network toxicity?
How do varying moderation strictness thresholds influence the migration of toxic users to less moderated platforms?

1.3 Current Challenges In Moderation

Moderation faces multi-faceted challenges:

Technical: Processing immense daily content volume [1], accurately identifying nuanced malicious content across different contexts [15].
Political & Cultural: Navigating political pressures, varying cultural norms leading to perceived unfairness [12], and accusations of bias [13], [26].
Behavioral (Migration): Strict moderation can cause users to "flock" to less moderated platforms [27], potentially creating concentrated "pockets" of extreme toxicity (e.g., Gab [18], 4chan [4]). This migration is a key dynamic this paper investigates [22].

2. Background

Moderation aims to remove harmful content like misinformation, hate speech, and explicit material. This paper focuses on toxicity, defined as "rude, disrespectful, or unreasonable comment that is likely to make you leave a discussion" [14].

Core Moderation Steps [23]:

Creation of Guidelines: Defining acceptable content via terms of service.
Violation Detection: Identifying harmful content using human reviewers, automated systems (e.g., ML/AI [1]), or hybrid approaches [13]. Challenges include volume, nuance, and cross-cultural understanding [15].
Policy Enforcement: Deciding how to handle violations, ranging from soft moderation (warnings, labels, quarantining) to hard moderation (temporary/permanent bans).

Enforcement Challenges:

Balancing safety with free expression.
Political and market pressures influencing policy [12].
User perception: Strictness can lead to backlash or migration [27], a driving force for movement to alternative platforms [22].

3. Related Work

Moderation Impacts

Research shows complex effects of moderation, particularly de-platforming:

Can reduce a toxic user's reach but may also lead to radicalization on alternative platforms [4].
Conflicting findings on user toxicity post-migration: some studies suggest a decrease (less 'trolling' opportunity?) [19], while users still post content banned elsewhere, often at higher volumes [19].
Highlights the intricate, sometimes counterintuitive, consequences of moderation beyond simple content removal.

Toxicity Research

Extensive work exists on understanding toxicity itself:

Development of taxonomies for hate/harassment [25].
Measurement and categorization of malicious tactics like flooding and abuse [17], [25].
Comparative studies of language and behavior on toxic vs. mainstream platforms (e.g., Gab, Parler, Twitter) [24].
Identifying characteristics distinguishing toxic from regular accounts [9].
Analysis of the evolution and spread of specific toxic movements (e.g., GamerGate [6]).

Agent-Based Modeling in Social Media

ABM is increasingly used to study social media:

The Construct framework (from CMU CASOS) is specifically designed for modeling social network dynamics, agent interactions, and information diffusion [7].
Prior work, such as Murdock et al. [20], has used ABM (Construct) to simulate misinformation spread across platforms and assess moderation impacts.
This paper extends these ABM approaches by explicitly incorporating a toxicity dimension for agents and messages, aiming to provide a more nuanced understanding of how moderation influences toxicity dynamics and user migration.

References Cited in Sections 1-3

[1]Alam et al. A survey on multimodal disinformation detection
[2]Ali et al. Understanding the effect of deplatforming on social networks
[3]Averza et al. Evaluating the influence of twitter bots via agent-based social simulation
[4]Bernstein et al. 4chan and /b/: An analysis of anonymity and ephemerality
[5]Beskow and Carley. Agent based simulation of bot disinformation maneuvers
[6]Chatzakou et al. Measuring# gamergate: A tale of hate, sexism, and bullying
[7]Dipple et al. Construct user guide
[8]DiResta et al. The tactics & tropes of the internet research agency
[9]ElSherief et al. Peer to peer hate: Hate speech instigators and their targets
[10]Feuerriegel et al. Research can help to tackle AI-generated disinformation
[11]Garibay et al. Deep agent: Studying the dynamics of information spread
[12]Gillespie. Custodians of the Internet: Platforms, content moderation...
[13]Gongane et al. Detection and moderation of detrimental content on social media platforms
[14]Kumar et al. Understanding the behaviors of toxic accounts on reddit
[15]Kumar et al. Designing toxic content classification for a diversity of perspectives
[16]Le et al. Adversarial socialbot learning via multi-agent deep hierarchical reinforcement learning
[17]Legucka. Russian disinformation: Old tactics-new narratives
[18]Lima et al. Inside the right-leaning echo chambers: Characterizing gab
[19]Mekacher et al. The systemic impact of deplatforming on social media
[20]Murdock et al. An agent-based model of cross-platform information diffusion and moderation
[21]Muller and Schwarz. From hashtag to hate crime: Twitter and anti-minority sentiment
[22]El Ouirdi et al. Social media conceptualization and taxonomy: A lasswellian framework / Ali et al. [2] driving force behind migration
[23]Singhal et al. Sok: Content moderation in social media, from guidelines to enforcement...
[24]Sipka et al. Comparing the language of qanon-related content on parler, gab, and twitter
[25]Thomas et al. Sok: Hate, harassment, and the changing landscape of online abuse / Kumar et al. [14] defines toxicity
[26]Veglis. Moderation techniques for social media content
[27]Young. How much is too much: the difficulties of social media content moderation
[28]Zeng et al. Adversarial socialbots modeling based on structural information principles

Methodology

This section outlines the methodology used to model the impact of content moderation on user behavior, individual platform toxicity, and overall network toxicity. The modeling of these dynamics is done using an Agent-Based Model built using the Construct framework from CMU's CASOS lab. This approach allows us to simulate the complex interactions between users and platforms and analyze the effects of different moderation strategies.

Construct Overview

Overview

The key components of this framework are users and social media platforms. The users are able to interact with one another and the social media platforms by reading content, posting, and reacting to content through actions like reposting or replying. The platforms facilitate this communication. Further information is stored in networks which store and update the relationships between users, the platform, and other entities.

For a more detailed explanation of the Costruct Social Media simulation, see prior papers which utilize the platform and the "Construct User Guide"

Platform Settings

Number of Agents: 100 agents were used
Number of Platforms: 3 platforms were used. The largest was the test platform, the second largest was the strictest, and the smallest was unmoderated
Time Steps: 365 days were simulated with each timestep representing a single day.

User Actions

Read existing posts
Create a new post on a given platform
Quote, Repost, or Reply to an existing message
Update it’s own parameters like it’s trust in other agents
Move between platforms

Selected Metric

Regression Coefficients (Significant)

Statistical coefficients and their significance

Variable	Coefficient	P-value
strictness	0.0070	9.14e-3
strategy_Full_Ban	0.0127	3.15e-26
strategy_No_Moderation	0.0150	6.57e-33
strategy_Partial_Ban	0.0066	5.42e-10
distribution_beta	-0.0200	1.37e-53
distribution_bimodal	0.0199	2.99e-53
distribution_normal	0.0140	1.78e-35
distribution_uniform	0.0204	1.45e-54

OLS Regression Summary

R-squared: 0.885

Adj. R-squared: 0.879

F-statistic: 145.0

F-statistic P-value: 6.12e-75

Overall model significance

ANOVA Summary

Strategy

F-statistic: 5.498

P-value: 3.42e-4

Distribution

F-statistic: 194.448

P-value: 1.25e-55

Strictness

F-statistic: 0.490

P-value: 0.613

Misses

F-statistic: 0.021

P-value: 0.979

Statistical Analysis

View statistics by different data slices

Statistics for strategy
Metric	Warning Partial Ban	Partial Ban	No Moderation	Warning	Full Ban
count	36.00	36.00	36.00	36.00	36.00
mean	0.04	0.05	0.06	0.05	0.06
std	0.01	0.02	0.02	0.01	0.02
min	0.02	0.02	0.02	0.02	0.02
25%	0.03	0.04	0.05	0.04	0.04
50%	0.05	0.06	0.07	0.05	0.06
75%	0.05	0.06	0.07	0.06	0.07
max	0.06	0.08	0.08	0.06	0.09

Selected Metric

Visulization Type

Visualization

Analysis Dimension

Analysis Granularity

Discussion

The results demonstrate the complex interplay between moderation strategies and user behavior across social media platforms. While strict moderation effectively reduces toxicity on individual platforms, the migration of users to less moderated platforms creates new challenges for the broader social media ecosystem.

Implications for Platform Governance

The findings suggest that effective content moderation requires coordination between platforms to prevent the concentration of toxic behavior in unmoderated spaces.

Limitations

Simplified user behavior models
Lack of access to real-world data for comparison
Limited consideration of external factors (e.g. pressures from governments or outside interest groups)
Assumption of perfect moderation implementation

Conclusion

This study provides evidence that while platform-specific moderation can be effective, the interconnected nature of social media platforms requires a more holistic approach to content moderation. Future research should focus on developing coordinated moderation frameworks that consider the systemic impact of platform policies.

Future Directions

Development of cross-platform moderation standards
Investigation of user rehabilitation strategies
Analysis of long-term ecosystem stability